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Abstract

Objective:

Fatigue, alertness and daytime sleepiness are complex perceived states with significant implications for health and safety. These states are not diagnostically specific, and they may, or may not share interrelated features. Potential commonality or discordance is challenging in clinical situations, especially in controversial neurologic disorders, as in cases of concussion/ mild traumatic brain injury (mTBI).

Methods:

We performed a diagnostic multivariable modeling study to explore associations between patients’ characteristics, results of imaging tests and clinical investigations, and the states of fatigue, alertness and daytime sleepiness. The intensity of fatigue, alertness and daytime sleepiness was measured using the standardized Fatigue Severity Scale (FSS), the Toronto Hospital Alertness Test (THAT), and the Epworth Sleepiness Scale (ESS). Univariate and multivariate linear regression models were used to explicate covariates of fatigue, alertness, and daytime sleepiness.

Results:

A total of 94 patients (45.20 ± 9.94 years; 61.2% males) with an established diagnosis of concussion/mTBI were included in the current analysis. Our results revealed that fatigue and alertness are associated with covariates within the domain of brain function integrity, and that daytime sleepiness is associated with cultural and physiological bodily states. In the final fully adjusted multivariable regression models, several covariates accounted for 57.2% of the fatigue variance, 41.2% of alertness variance, and 27.1% of the sleepiness variance. While fatigue and alertness share covariates, daytime sleepiness represents a distinct construct in persons with concussion/mTBI.

Conclusions:

Our findings challenge the commonly held view that fatigue, alertness and daytime sleepiness are perceived states on the same continuum. The implications of this finding have direct relevance to the clinical approach towards patients presenting with fatigue, impaired alertness or excessive daytime sleepiness after concussion/mTBI.

Introduction

Fatigue, alertness and daytime sleepiness are
complex perceived states with significant
implications for health and safety [1-3]. The
Stedman Medical Dictionary defines fatigue
as, “(1) that state, following a period of mental
or bodily activity, characterized by a lessened
capacity for work and reduced efficiency of
accomplishment, usually accompanied by a
feeling of weariness, sleepiness, or irritability;
may also supervene when, from any cause,
energy expenditure outstrips restorative
processes and may be confined to a single organ,
and (2) sensation of boredom and lassitude due
to absence of stimulation, monotony, or lack
of interest in one’s surroundings”[4]. The same
dictionary defines sleepiness as, “(1) drowsiness; an
inclination to sleep, and (2) a condition of semiconsciousness
approaching coma” [5]. ‘Alertness’
is not defined in the dictionary, but appears under
‘lethargy’, defined as, “a relatively mild impairment
of consciousness causing reduced alertness and
awareness; this condition has many causes but is
ultimately due to generalized brain dysfunction”[4].
Clearly, the three terms, fatigue, alertness, and sleepiness are not diagnostically specific. They
may or may not share interrelated features, and
this makes it difficult to deal clinically with
neurological disorders in which these symptoms
are present. This is particularly relevant to the
diagnostic uncertainty surrounding long-term
neurological disorders that are difficult to explain
– for example, when the injury has involved
relatively limited force to the head and limited
injury to the brain’s structure, as in concussion,
a most common form of mild traumatic brain
injury (mTBI) [5-7].

In the United States, the most widely accepted
criteria for concussion/mTBI is proposed by the
American Congress of Rehabilitation Medicine,
which defines it as, “a physiological disruption
of brain function as a result of a traumatic
event as manifested by at least one of the
following: Alteration of mental state, loss of
consciousness (LOC), loss of memory or focal
neurological deficits that may or may not be
transient” [8].

Concussion/mTBI comprises more than 85%
of all medically-treated TBIs, yet it remains
among the most challenging and controversial
of neurological disorders [9-11]. While many
patients recover fully within days or weeks
[12-14], it is estimated that at least 15% will
experience delayed recovery, with disabling
symptoms persisting beyond three months [15].
Many of these symptoms are not specific to TBI
and are common in the general population,
including fatigue, difficulty with attention/
maintaining alertness, and daytime sleepiness
[16]. Despite great interest in the study of
perceived states, it is unclear whether any
of these states (fatigue, alertness, or daytime
sleepiness) represents a physiological state
or a composite symptom cluster that varies
depending on the mechanism of the concussion/
mTBI, premorbid medical state, psychiatric
morbidity, psychosocial issues, or is related to
medication/drug effects. In other words, each
of these states (i.e., fatigue, daytime sleepiness,
or impaired alertness) could be overt expression
of multiply-determined neurophysiological,
psychological, and behavioral processes that contribute to a final common pathway of a
negative perceived state post-injury (Figure l).
Therefore, attention to potential pathways is key
to determining how to manage post-concussive
symptoms and to search for effective solutions.
Furthermore, understanding how fatigue,
alertness, and daytime sleepiness are related or
distinct is important for a broader health and
safety management system in concussion/mTBI,
especially regarding release to return to work
after injury (i.e., assessment of fitness for duty).
The concurrent comparison of the factors
associated with perceived fatigue, alertness,
and daytime sleepiness will indirectly indicate
whether these three constructs constitute
a symptom cluster in concussion/mTBI or
have different networks. Therefore, our aim
was to study the three perceived states among
patients experiencing delayed recovery from
concussion/mTBI. On the basis of previous
research into factors related to fatigue,
alertness, and daytime sleepiness in TBI [17- 21], we hypothesized that: (1) fatigue and
alertness are closely related constructs, and
substantial overlap in factors associated with
these states would be observed; (2) daytime
sleepiness in concussion/mTBI is a distinct
(from fatigue and alertness) construct; and (3)
fatigue, alertness, and daytime sleepiness have
a sleep-related component as a shared factor.

Material and Methods

The study protocol was approved by ethics
committees at clinical and academic institutions
with which the authors were affiliated. The study
complied with the principle of the Declaration
of Helsinki. To explicate the physiological,
pathological and behavioral processes of fatigue,
alertness, and daytime sleepiness in concussion/
mTBI, we grouped elements across simpler
organizational themes that allowed us to begin
with associations, followed by statistical models
at various hierarchical levels of aggregation.
We followed the Transparent Reporting of a
Multivariable Prediction Model for Individual
Prognosis or Diagnosis (TRIPOD) guidelines
[22].

▪ Procedure and participants

The Neurology Service of the largest rehabilitation
teaching hospital in Canada has a contractual
agreement with the insurer (i.e., Workers Safety
and Insurance Board (WSIB)) to provide expert
diagnostic opinions for persons who have or
are suspected to have sustained neurological injuries at work. Injured persons were recruited
at the admission to the insurer’s clinic. Initial
contact was made with 178 subjects, of whom
110 provided written consent to participate and
completed the required assessments. At the same
time participants underwent comprehensive specialty
investigations (i.e., psychiatry, neurology, occupational
therapy, physiotherapy, and neuropsychology) and
neuroimaging testing for establishing TBI diagnoses.
The researchers were blinded to the participant’s
diagnosis until their medical charts became available
for review, after clinical assessments were complete.
All participants were also asked for consent to access
their pre-morbid clinical and insurer’s files, and
all gave written permission. To assess our sample’s
representativeness indirectly, we compared it to a
consecutive sample of persons (n = 294) who were
referred and assessed in the same clinic during 2003
[23]. No significant differences were observed in
injury severity, sex, age, or clinical diagnosis. To
maintain sample homogeneity in terms of injury
severity, we used data for persons (n = 94) with
an established diagnosis of concussion/ mTBI
(Appendix A).

▪ Instruments and measures

Standardized patient-reported (PR) measures
based on previous work as most suited to
persons with TBI [16,21] were used to assess
each participant’s opinion and appraisal of his/
her levels of fatigue, alertness, and sleepiness.
Additional PR measures covered sleep-,
clinical-, injury- related, and behavioral variables
pertinent to our hypotheses [16,21]. Medical
files provided data on brain injury mechanism,
presence of LOC, post-traumatic amnesia (PTA),
neuroimaging findings, and clinical diagnosis
pre- and post-injury, including the Diagnostic
and Statistical Manual of Mental Disorders
(DSM)-IV-TR [24]. Injury-related variables
were also collected from the participants’ WSIB
files, and included working status at the time of
investigation, previous work-related injuries (if
any), employer-patient relations, and insurerpatient
relations Measurements by clinicians
(i.e., all diagnostic investigations and clinical
assessments) and those obtained directly from
the participants (i.e., all PRs) were collected
within a short period during which no treatment
was commenced. Detailed descriptions of the
studied independent variables are presented in
Appendix B.

The primary outcome variables were level of
fatigue, alertness and sleepiness as determined by
the Fatigue Severity Scale (FSS) [25], Toronto Hospital Alertness Test (THAT) [26] and Epworth
Sleepiness Scale (ESS) [27]. For information on the
psychometric properties of the PR measures used,
we refer the reader to published studies [16,21].

Means and standard deviations or medians
and ranges were used for continuous data and
frequency counts for categorical data. The
FSS, THAT and ESS scores were skewed in our sample; however, their residuals (a more
important ‘test’ for normality) were distributed
normally [28]. To detect potential collinearity
in the modeling process, we used Spearman’s
correlation coefficients for all a priori-defined associations between continuous variables and
one-way analysis of variance for categorical/
binary explanatory variables with two or more
levels. To assess the inflation of variances of
the estimated coefficients, we used the variance
inflation factor (VIF); a VIF > 6 was considered
to indicate collinearity [29]. Because one of our independent variables (i.e., measure of
depression by the Patient Health Questionnaire
(PHQ)- features the item ‘feeling tired or having
little energy’, which is arguably related to the
constructs under study (i.e., fatigue, alertness, and
sleepiness), we removed this item from the total
PHQ-9 score when reporting associations. We
applied stepwise multiple linear regressions with
elimination to build models for each category
of variables, grouped by (1) socio-demographic;
(2) brain-injury related; (3) medical; (4) sleeprelated
(including circadian-driven mechanism);
and (5) medication/substance effects.

All hypothesized variables associated with
outcomes of interest at statistically significant
level p≤.2, and those consistently reported in
the literature being associated with constructs
under the study, were included initially; all
variables identified as significant at p ≤ .1 were
included in the final model. Sex, age, and time
of assessment were included in every model
that used PR measures. Several items within
the PR measures (~5%) were not completed by
participants. We examined missing items and
did not observe a relationship with responses
on other items within these PR measures.
Therefore, we treated them as missing at
random, and used single data imputation to
estimate the values of missing items [30]. Our
sample size was sufficient to permit accurate
estimation of regression coefficients, standard
errors, and confidence intervals in all linear
regression models [31].

▪ Power calculation

This study (see study context section) recruited
110 persons with TBI, of which 94 participants
were diagnosed with mTBI/concussion. A
published series of Monte Carlo simulation
studies suggest that linear regression models
will produce accurate (i.e., relative bias less
than 10%) estimation of regression coefficients,
standard errors, and confidence intervals with a
sample size of at least two subjects per variable
[31]. The present study two or more ensured
the recommended number of participants per
independent variable and, therefore, secured
sufficient power in each multivariable model.

Results

Table 1a,b present the characteristics
of the 94 participants (45.20 ± 9.94 years;
61.2% males) with a diagnosis of concussion/
mTBI, established upon completion of clinical
investigations. Seventy-four (78%) were born
in Canada, 69 (73%) were single/widowed or
divorced, and 56 (60%) had post-secondary
degrees. Median time since injury (TSI) was
197 days [interquartile range, 139-416]. The
major mechanisms of injury were falls (from
elevation 19% and same level 18%), being
struck by (19%) or against (17%) an object,
motor vehicle incidents (13%), and being
struck by a living subject (10.5%). Among
persons with documented LOC and/or PTA,
31% had experienced LOC and 25% PTA.
Previous head injuries were documented in
the files of 23 persons (25%). Eighty-four
participants underwent MRI or CT imaging;
none exhibited trauma-related brain changes;
scattered foci of hyper-intensity were detected in
27 participants (32%). Many of the participants
were diagnosed with one or more DSM-IV TR
disorders (Table 1a,b), including cognitive (63%),
adjustment (51%), anxiety (45%) mood (42%),
somatoform (29%), substance-related (15%),
and sleep (10%) disorders; eight of the latter had
sleep-related breathing disorders.

Category

Variables

n (%N*)

FSS score mean (SD)

P-value

THAT score mean (SD)

P-value

ESS score mean (SD)

P-value

Socio-demographic, psychosocial

Sex

Male

58 (62)

43.50 (15.89)

0.093

21.30 (9.22)

0.169

8.53 (5.69)

0.770

Female

36 (38)

48.81 (12.77)

19.06 (8.88)

8.89 (5.70)

Born in Canada

Yes

75 (80)

44.56 (15.25)

0.211

20.66 (9.72)

0.644

9.79 (5.48)

<0.001

No

19 (20)

49.37 (13.23)

19.53 (8.73)

4.26 (4.05)

English first language

Yes

77 (82)

44.84 (15.30)

0.345

20.53 (9.40)

0.838

9.35 (5.54)

0.012

No

17 (18)

48.65 (13.05)

20.00 (10.17)

5.59 (5.50)

Marital status

Married/common law

69 (73)

45.25 (14.72)

0.761

18.84 (9.95)

0.330

8.90 (5.32)

0.231

Single/divorced/widowed

25 (27)

46.32 (15.77)

21.02 (9.33)

8.21 (6.69)

Dependent children in household

Yes

55 (59)

45.67 (13.63)

0.910

20.90 (10.06)

0.399

9.75 (5.37)

0.318

No

39 (41)

45.33 (16.77)

20.09 (9.14)

8.57 (5.71)

Education

=High school

34 (36)

46.03 (14.33)

0.827

18.01 (11.02)

0.211

6.94 (6.22)

0.835

High school-college, professional diploma

32 (34)

45.88 (15.77)

21.08 (10.55)

7.88 (5.81)

University and higher

24 (27)

45.99 (16.88)

19.55 (8.59)

6.94 (5.94)

Current working status

Working full-/part time

54 (57)

44.55 (14.65)

0.586

22.40 (10.58)

0.082

9.09 (5.92)

0.404

On disability/laid off

40 (43)

46.26 (15.22)

18.94 (8.38)

8.10 (5.32)

Occupation at injury

Office

48 (51)

46.60 (14.66)

0.878

19.75 (8.94)

0.772

9.46 (5.45)

0.176

Laborers

46 (49)

45.13 (15.27)

20.31 (9.68)

7.87 (5.86)

Accident involvement due to sleepiness

Yes

8 (9)

45.14 (15.25)

0.406

15.00 (8.40)

0.090

8.00 (5.76)

0.728

No

86 (91)

49.75 (10.66)

20.94 (9.47)

8.73 (5.67)

Family difficulties, by DSM-IV-TR

Yes

59 (62)

44.95 (15.07)

0.626

20.59 (8.82)

0.829

9.49 (5.40)

0.068

No

28 (38)

46.51 (14.84)

20.15 (10.70)

7.29 (5.91)

Previous WSIB claims

Yes

8 (9)

49.50 (14.83)

0.435

19.25 (10.12)

0.715

9.75 (5.22)

0.576

No

86 (91)

46.16 (14.97)

20.54 (9.49)

8.57 (5.70)

Probable/possible malingering, by DSM-IV-TR

Yes

14 (16)

51.64 (13.32)

0.109

15.86 (6.95)

0.084

11.36 (6.16)

0.060

No

74 (84)

44.72 (14.89)

20.58 (9.60)

8.25 (5.48)

Tension with employer

Yes

34 (36)

46.62 (13.35)

0.598

19.67 (9.24)

0.568

10.09 (6.07)

0.067

No

60 (64)

44.92 (15.82)

20.85 (9.68)

7.87 (5.31)

Tension with insurer

Yes

14 (15)

47.21 (15.33)

0.650

18.29 (10.27)

0.362

9.64 (6.51)

0.489

No

80 (85)

45.24 (14.76)

20.81 (9.37)

8.50 (5.53)

Injury-related

Mechanism of injury

Caught, crushed, jumped, pinched

Yes

4 (4)

41.00 (15.66)

0.667

30.50 (12.02)

0.130

4.00 (4.24)

0.240

No

90 (96)

46.63 (14.99)

20.28 (9.39)

8.77 (5.78)

Struck by inanimate object

Yes

18 (19)

46.11 (13.63)

0.856

18.77 (6.31)

0.398

9.11 (5.97)

0.716

No

76 (81)

45.40 (15.30)

20.84 (10.10)

8.57 (5.62)

Struck by another person

Yes

10 (9)

45.90 (10.33)

0.935

16.30 (7.53)

0.146

9.60 (6.95)

0.585

No

84 (91)

45.49 (15.43)

20.93 (9.62)

8.56 (5.53)

Struck against object/structure

Yes

16 (17)

47.75 (14.16)

0.008

19.45 (8.52)

<0.001

9.00 (4.14)

0.864

No

78 (83)

32.37 (17.54)

30.88 (13.22)

8.64 (5.80)

Exposure to explosion

Yes

2 (2)

41.00 (9.90)

0.667

NC**

NC**

10.00 (5.66)

0.739

No

92 (98)

45.63 (15.04)

8.64 (5.69)

Motor-vehicle accident

Yes

12 (13)

43.75 (18.32)

0.660

20.25 (8.99)

0.944

8.33 (6.19)

0.916

No

82 (87)

45.79 (14.48)

20.46 (9.62)

8.65 (5.62)

Fall from elevation

Yes

18 (19)

41.33 (16.80)

0.186

22.61 (10.92)

0.280

8.72 (6.02)

0.966

No

76 (81)

46.53 (14.39)

19.91 (9.12)

8.66 (5.62)

Fall from same level

Yes

17 (18)

51.35 (12.72)

0.076

17.88 (7.99)

0.223

6.94 (4.92)

0.166

No

77 (82)

44.25 (15.44)

21.00 (9.75)

9.05 (5.77

Loss of consciousness

Yes

29 (31)

44.43 (14.67)

0.352

19.56 (11.34)

0.699

9.57 (5.12)

0.297

No

65 (69)

45.87 (14.22)

20.22 (9.67)

8.26 (5.88)

Post-traumatic amnesia

Yes

21 (25)

43.62 (15.40)

0.508

21.19 (9.74)

0.679

8.86 (4.70)

0.865

No

73 (75)

46.08 (14.85)

20.21 (9.48)

8.62 (5.94)

Previous head trauma

Yes

23 (25)

44.71 (14.52)

0.174

21.50 (8.63)

0.366

9.73 (5.16)

0.208

No

71 (75)

45.77 (15.66)

20.19 (9.82)

8.41 (5.82)

Non-specific head MRI or CT findings

Yes

27 (32)

44.04 (14.60)

0.476

20.77 (11.27)

0.499

7.34 (4.87)

0.188

No

57 (68)

46.55 (15.01)

19.26 (8.39)

9.10 (5.76)

Not noted^

Non-specific (degenerative) neck X-ray/CT findings

Yes

47 (58)

48.15 (15.01)

0.084

19.25 (9.09)

0.085

8.17 (5.67)

0.689

No

35 (42)

40.60 (12.53)

24.07 (9.85)

8.80 (3.71)

Not noted^

Hematoma/lacerations/head bones’ fracture

Yes

22 (23)

43.36 (14.06)

0.439

22.18 (10.47)

0.325

8.13 (5.35)

0.616

No

72 (77)

46.19 (15.22)

19.89 (9.18)

8.33 (5.78)

Comorbid conditions, diagnosed, by self-report

Arthritis

Yes

34 (37)

46.74 (14.14)

0.658

18.24 (7.90)

0.125

9.71 (5.77)

0.201

No

59 (63)

45.32 (15.12)

21.28 (9.71)

8.14 (5.60)

Sleep disorder (any)

Yes

10 (11)

50.50 (13.15)

0.268

19.70 (10.51)

0.798

12.56 (4.67)

0.033

No

84 (89)

44.94 (15.08)

20.52 (9.43)

8.31 (5.64)

Diabetes mellitus

Yes

5 (5)

47.00 (20.04)

0.823

20.40 (15.17)

0.992

4.80 (6.38)

0.117

No

89 (95)

45.45 (14.73)

20.43 (9.20)

8.89 (5.58)

Heart disease

Yes

6 (6)

44.54 (14.65)

0.915

22.50 (12.45)

0.584

10.33 (6.50)

0.460

No

88 (94)

45.55 (15.76)

20.29 (9.33)

8.56 (5.63)

Malignancy

Yes

2 (2)

51.00 (9.90)

0.603

21.50 (10.61)

0.873

9.00 (7.07)

0.934

No

92 (98)

45.41 (15.03)

20.41 (9.53)

8.66 (5.68)

DSM-IV-TR disorders

Adjustment disorder

Yes

45 (51)

47.24 (13.58)

0.358

19.07 (8.95)

0.442

8.62 (6.75)

0.830

No

43 (49)

44.33 (16.00)

20.62 (9.82)

8.88 (7.32)

Anxiety disorder

Yes

40 (45)

48.1511.60)

0.179

17.56 (8.70)

0.042

8.55 (5.77)

0.765

No

48 (55)

43.88 (16.90)

21.65 (9.55)

8.92 (5.64)

Cognitive disorder

Yes

55 (63)

43.53 (16.10)

0.060

21.27 (10.22)

0.047

8.16 (6.68)

0.212

No

33 (37)

49.63 (11.68)

17.27 (7.18)

9.73 (7.63)

Mood disorder

Yes

37 (42)

46.51 (13.32)

0.709

18.59 (6.40)

0.480

8.76 (6.02)

0.993

No

51 (58)

46.31 (15.90)

20.23 (10.17)

8.75 (5.47)

Personality traits

Cluster B

Yes

15 (17)

44.56 (13.88)

0.300

20.25 (8.99)

0.779

8.35 (5.87)

0.685

No

77 (83)

46.12 (14.32)

19.78 (10.02)

7.96 (5.77)

Cluster C

Yes

42 (47)

45.98 (14.41)

0.902

21.32 (8.88)

0.664

8.22 (6.88)

0.055

No

50 (53)

45.58 (15.55)

20.33 (9.27)

7.12 (5.33)

Somatoform disorder

Yes

26 (28)

46.62 (15.71)

0.746

19.27 (7.85)

0.724

10.35 (6.10)

0.087

No

62 (72)

45.48 (14.52)

20.05 (9.98)

8.08 (5.39)

Sleep disorder

Yes

7 (10)

44.86 (15.53)

0.859

21.57 (11.90)

0.608

11.40 (5.52)

0.107

No

81 (90)

45.90 (14.83)

19.66 (9.17)

8.35 (5.62)

Substance-related disorder

Yes

13 (15)

40.92 (14.28)

0.198

22.42 (9.50)

0.302

7.69 (4.48)

0.469

No

75 (85)

46.66 (14.82)

19.40 (9.33)

8.93 (7.61)

Symptom load

Balance issues

Yes

44 (47)

47.16 (14.59)

0.324

18.00 (7.99)

0.021

7.98 (5.34)

0.268

No

50 (53)

44.10 (15.22)

22.52 (10.23)

9.28 (5.92)

Bodily pain

Yes

32 (34)

46.22 (14.61)

0.750

18.72 (10.05)

0.210

7.56 (5.67)

0.174

No

62 (66)

45.18 (15.19)

21.33 (9.15)

9.24 (5.62)

Cognitive complaints

Yes

67 (71)

47.81 (14.40)

0.019

19.46 (8.18)

0.115

9.11 (5.70)

0.244

No

27 (29)

39.89 (14.98)

22.92 (12.08)

7.59 (5.53)

Mood disturbance

Yes

62 (66)

45.71 (14.43)

0.873

19.89 (17.67)

0.448

8.32 (5.77)

0.410

No

32 (34)

45.19 (16.08)

21.47 (10.99)

9.34 (5.49)

Head and/or neck pain

Yes

87 (93)

45.84 (14.63)

0.485

19.88 (9.37)

0.051

8.52 (7.30)

0.359

No

7 (7)

41.71 (19.20)

27.14 (8.95)

10.57 (5.03)

Photo-/phonophobia

Yes

14 (15)

49.86 (13.05)

0.242

19.29 (8.58)

0.627

9.07 (5.90)

0.776

No

80 (85)

44.78 (15.18)

20.63 (9.68)

8.60 (5.66)

Sleep-related issues

Yes

59 (63)

44.25 (14.50)

0.284

20.44 (9.93)

0.981

7.36 (5.98)

0.003

No

35 (37)

47.69 (14.77)

20.40 (8.86)

10.89 (5.71)

Snoring (STOP-Bang)

Yes

71 (76)

45.03 (14.47)

0.890

20.61 (9.57)

0.503

8.46 (5.77)

0.859

No

23 (24)

44.50 (16.74

19.05 (7.52)

9.10 (2.24)

Observed pause in breathing in sleep (STOP-Bang)

Yes

18 (19)

51.17 (10.25)

0.046

18.83 (8.65)

0.460

10.94 (5.76)

0.086

No

76 (81)

43.37 (15.51)

20.23 (9.27)

8.40 (5.52)

Medication intake

Antihistamines

Yes

23 (26)

48.48 (14.14)

0.278

19.78 (10.76)

0.708

7.78 (5.49)

0.390

No

71 (74)

44.58 (15.15)

20.64 (9.11)

8.96 (5.73)

Benzodiazepines

Yes

12 (13)

48.00 (13.99)

0.543

16.25 (9.35)

0.102

7.33 (5.58)

0.384

No

82 (87)

45.17 (15.11

21.05 (9.41)

8.87 (5.68)

Narcotic analgetics

Yes

21 (22)

47.19 (13.80)

0.566

18.57 (8.80)

0.311

9.33 (6.67)

0.546

No

73 (78)

45.05 (15.29)

20.97 (9.68)

8.48 (5.38)

Tricyclic antidepressants

Yes

27 (29)

51.74 (12.30)

0.009

18.96 (7.64)

0.342

9.15 (6.49)

0.606

No

67 (71)

43.02 (15.24)

21.03 (10.15)

8.48 (5.34)

Serotonin reuptake inhibitors (SSRI)

Yes

14 (15)

44.43 (15.03)

0.766

18.57 (13.05)

0.430

9.29 (4.80)

0.662

No

80 (85)

45.73 (14.99)

20.76 (9.50)

8.56 (5.82)

Recreational substance use

Yes

9 (12)

43.56 (10.61)

0.625

21.00 (7.53)

0.853

9.11 (5.18)

0.869

No

83 (88)

46.08 (15.03)

20.38 (9.68)

8.78 (5.71)

Sleep timing, bed time (weekday)

Regular (<60 min)

50 (53)

44.37 (14.77)

0.434

22.36 (9.10)

0.278

8.40 (5.52)

0.356

Irregular

60-120 min

35 (37)

52.54 (16.44)

19.27 (8.80)

11.40 (5.62)

>120 min

9 (10)

48.69 (15.88)

20.32 (10.78)

9.87 (6.67)

Wake timing (weekday)

Regular (<60 min)

63 (67)

49.44 (15.88)

0.193

21.21 (9.10)

0.129

9.78 (5.88)

0.246

Irregular

60-120 min

29 (31)

46.58 (15.42)

19.41 (8.19)

9.40 (6.49)

>120 min

2 (2)

53.44 (12.77)

18.55 (11.77)

10.87 (5.67)

Taking nap during the day

Yes

59 (64)

46.08 (15.34)

0.637

19.73 (9.39)

0.350

10.02 (8.56)

0.002

No

34 (36)

44.56 (14.34)

21.65 (9.70)

6.29 (4.91)

Completion of assessment in the afternoon vs morning or night

Yes

41 (44)

41.54 (14.69)

0.027

21.59 (11.07)

0.149

8.81 (5.16)

0.853

No

53 (56)

48.34 (14.48)

18.79 (7.42)

8.58 (6.09)

Table 1a: Characteristics of the study population and corresponding fatigue, alertness and daytime sleepiness scores for
binary and categorical variables.

Variables

Mean (SD)/median (Q3-Q1)

FSS score mean (SD) Rho

P-value

THAT score mean (SD) Rho

P-value

ESS score mean (SD) Rho

P-value

Age, years

45.2 (9.94)

0.103

0.301

-0.21

0.845

-0.003

0.974

Weekly income, $CAD

1056 (510)

-0.206

0.047

0.089

0.392

-0.045

0.665

Time since injury, days

197 (416-139)

-0.243

0.021

0.114

0.275

-0.156

0.133

Number of comorbid conditions

2.22 (1.04)

0.16

0.123

-0.106

0.314

0.087

0.402

Patient-reported (PR) states

Insomnia (ISI)

17.47 (6.32)

0.52

<0.001

-0.394

<0.001

0.047

0.652

Anxiety (HADS-A)

10.71 (4.74)

0.327

0.001

-0.376

<0.001

0.127

0.222

Depression (PHQ-9)*

14.31 (6.31)

0.551

<0.001

-0.454

<0.001

0.123

0.239

Pain (VAS-P), current

5.02 (2.40)

0.263

0.011

-0.257

0.013

-0.066

0.529

Restless legs (RLQ)

3.15 (2.42)

0.219

0.035

-0.339

<0.001

0.064

0.541

Narcolepsy (SNS)

3.15 (2.42)

0.051

0.626

-0.092

0.378

-0.066

0.526

Total number of sleep-related breathing disorder risk factors (STOP-Bang)

4.19 (1.67)

-0.013

0.898

0.033

0.757

0.063

0.540

Substances

Alcohol intake, daily (portion(s) of beer, wine, or liquor)

0.40 (1.08)

-0.025

0.813

-0.025

0.813

0.122

0.242

Coffee, servings a day

2.10 (1.65)

0.066

0.528

-0.075

0.474

0.048

0.675

Number of prescribed medications

1.10 (0.96)

0.189

0.069

-0.156

0.136

0.025

0.807

Body mass index

28.68 (5.14)

-0.084

0.420

0.157

0.133

-0.051

0.624

Total sleep time

6.22 (2.16)

-0.066

0.528

-0.136

0.197

0.010

0.931

Sleep efficiency

0.71 (0.21)

-0.117

0.210

0.14

0.181

-0.155

0.138

Table1b: Characteristics of the study population and corresponding fatigue, alertness and daytime sleepiness scores for
continuous variables.

▪ Bivariate analyses

The mean total FSS score was 45.53 ± 14.92,
with 43.02 ± 15.89 for men and 48.81 ± 12.77
for women (where scores ≥ 36 indicate clinically
significant fatigue [25]). The mean alertness
score on THAT was 20.43 ± 9.49, with 21.30
± 9.22 for men and 19.06 ± 8.88 for women
(where scores ≤ 20.5 indicate impaired alertness
[26]). The mean sleepiness score on the ESS was
8.67 ± 5.66, with 8.53 ± 5.69 for men and 8.89 ± 5.70 for women (where scores ≥10 indicate
excessive sleepiness [27]). The total FSS scores
were negatively associated with the total THAT
scores (p<.001) and positively with the total
ESS scores (p=.014). There were no associations
between the total THAT and total ESS scores
(p=.257). The internal consistency of the FSS in
our sample, as measured by Cronbach’s α, was
.95, the THAT was .85, and the ESS was .78.

▪ Fatigue

Persons injured by striking against an object/
structure had significantly higher FSS total
scores (47.75 ± 14.16 vs 32.37 ± 17.51, p=.008)
than those with other injury mechanisms.
Participants with cognitive complaints had
significantly higher FSS scores (47.81 ± 14.40 vs 39.89 ± 14.98, p=.019) than those without.
There were significant differences in the FSS
scores between persons who did and did not
take tricyclic antidepressants (TCAs) and those
who did and did not paused during breathing in
sleep (51.74 ± 12.30 vs 43.02 ± 15.24, p=.009
and 51.17 ± 10.25 vs 43.37 ± 15.51, p=.046,
respectively). Participants who completed PR
measures in the afternoon had significantly
lower scores (41.54 ± 14.69 vs 48.34 ± 14.48,
p=.027) than those who completed them in the
morning or night. The Spearman’s correlation
coefficients for continuous variables were as
follows: the FSS scores were negatively correlated
with TSI (rho=-.243, p=.021) and pre-injury
weekly salary (rho=-.206, p=.047), and positively
with number of injuries within the past five
years (rho=.317, p=.001), insomnia (rho=.520,
p<.001), depression (rho=.551, p<.001), anxiety
(rho=.327, p=.001), pain (rho=.263, p=.011)
and restless legs (RL) (rho=.219, p=.035). There
were no significant effects among the other
independent variables.

▪ Alertness

Participants injured by striking against an object/
structure had significantly lower THAT total
scores (19.45 ± 8.52 vs 30.88 ± 13.22, p<.001)
than those with other injury mechanisms. THAT
scores differed significantly between persons who
did and did not report balance issues and head
and neck pain at the time of assessment (18.00
± 7.99 vs 22.52 ± 10.23, p=.021 and 19.88 ±
9.37 vs 27.14 ± 8.95, p=.051, respectively).
Persons with DSM-IV-TR anxiety disorder
had significantly lower THAT scores (17.56 ±
18.70 vs 21.65 ± 9.55, p=.042), and those with
cognitive disorders significantly higher ones
(21.27 ± 10.22 vs 17.27 ± 7.18, p=.047), than those without these diagnoses. The Spearman’s
correlation coefficients for continuous variables
showed the THAT scores were negatively
associated with insomnia, depression, anxiety
(rho=.- 394, rho=.-454 rho=.-376, respectively,
p<.001), pain (rho=.-257, p=.013) and RL
(rho=.-339, p<.001). There were no significant
effects among the other independent variables.

▪ Sleepiness

Persons born in Canada and those with English
as first language had significantly higher ESS total
scores (9.79 ± 5.48 vs 4.26 ± 4.05, p<.001 and
9.35 ± 5.54 vs 5.59 ± 5.50, p=.012, respectively)
than others. Persons who reported being
diagnosed with sleep disorder had significantly
higher ESS scores (12.56 ± 4.67 vs 8.31 ± 5.64,
p=.033) than those without these diagnoses. The
ESS scores differed significantly between persons
who reported sleep difficulties and those who did
not (7.36 ± 5.98 vs 10.89 ± 5.71, p=.003) and
those napping during the day and those who did
not (10.02 ± 8.56 vs 6.29 ± 4.91, p=.002). No
other significant correlations were observed.

The complete results of ANOVA for binary
and categorical variables and the Spearman’s
correlation coefficients for continuous variables
and their associated values with fatigue, alertness
and sleepiness are given in Table 1a,b.

▪Multivariable regression analyses

Several multivariate linear regression analyses were
used to assess how the covariates within sociodemographic,
brain-injury-, medical, sleep-, and
medication/substance effect-related categories were
associated with the outcome measures (i.e. fatigue,
sleepiness, and alertness). We fitted our stepwise
regression models on the basis of the bivariate
analyses, associations reported in the literature,
and pre-defined hypotheses (Figures 2-4). All our
models were adjusted for age and sex, and those
that utilized the PR measure were also adjusted
for assessment time. We did not observe VIF >
4 for any covariate, suggesting that collinearity
did not contribute to the change in regression
estimates. The final regression model tested
covariates of fatigue, alertness, and sleepiness
identified in the five preliminary models at
p ≤ .1. Following stepwise selection and using a
threshold level of significance of p ≤ .05, the final
fully-adjusted model of fatigue explained 57.2%
of the variance and contained six variables:
depression severity (β=.947, p<.001), TSI (β=-
.004, p<.001), insomnia (β=.529, p=.006),
completion of the PR measure in the afternoon (vs morning or night) (β=-7.083, p=.008), use of
TCAs (β=6.232, p=.015) and number of injuries
within the past five years (β=7.443, p=.010)
(Table 2). The final fully- adjusted model of
alertness explained 41.2% of the variance and
contained four variables: depression severity (β=-
0.694, p<.001), TSI (β=0.002, p=.044), balance
issues (β=-5.816, p=.009), RL severity (β=-2.002,
p=.003) and male sex (β=3.439, p=.031). Finally,
the final fully- adjusted model of sleepiness
explained 27.1% of the variance and contained
four variables: born in Canada (β=3.750, p<.001),
napping during the day (β=2.893, p=.009),
reporting sleep-related issues (β=-3.311, p=.009),
and DSM-IV-TR somatoform disorder (β=2.423,
p=.037) (Table 2, Appendix C).

Figure 2: Flow diagram depicting the stepwise multiple regression analysis of fatigue.
**age, sex, and time of completion of assessment (afternoon vs morning or night) were input in all models that used PR measures.

Figure 3: Flow diagram depicting the stepwise multiple regression analysis of alertness.
**age, sex, and time of completion of assessment (afternoon vs morning or night) were included in all models that used PR measures.

Figure 4:Flow diagram depicting the stepwise multiple regression analysis of sleepiness.
**age, sex, and time of completion of assessment (afternoon vs morning or night) were included in all models that used PR measures.

Fatigue model

Variable

β Coefficient

SE

P Value

Partial R2

Model R2/AdjR2

#1 Socio-demographic

Age

0.0006

0.0003

0.071

0.016*

0.016

#2 Clinical

Depression

0.947

0.205

<0.001

0.356

0.372

Use of tricyclic antidepressants

6.232

2.412

0.015

0.042

0.414

#3Injury- related

Time since injury

-0.004

0.001

<0.001

0.083

0.497

#4 Psychosocial

# injuries w/in 5 yr

7.443

2.393

0.010

0.033

0.530

#5 Sleep-/circadian rhythm-related

Insomnia

0.529

0.230

0.006

0.047

0.577

Afternoon assessment (vs morning or night)

-7.083

2.105

0.008

0.041

0.600/
0.572

Alertness model

Variable

β Coefficient

SE

P Value

Partial R2

Model R2/AdjR2

#1 Socio-demographic

Sex, male

3.439

1.566

0.031

0.031

0.031

#2 Clinical

Depression

-0.694

0.120

<0.001

0.255

0.286

Balance issues

-5.816

1.591

0.009

0.052

0.338

#3 Injury- related

Time since injury

0.00165

0.0008

0.044

0.037

0.375

Falls from same level

3.419

2.112

0.109

0.017*

0.375

#4 Sleep-/circadian rhythm-related

Restless legs

-2.002

0.626

0.003

0.063

0.438/
0.412

Daytime sleepiness model

Variable

β Coefficient

SE

P Value

Partial R2

Model R2/AdjR2

#1 Socio-demographic

Born in Canada

3.750

1.361

<0.001

0.135

0.135

#2 Clinical

Diagnosed sleep disorder

2.985

1.718

0.086

0.025*

0.160

#3 Psychosocial

Axis-IV-TR somatoform disorder

2.423

1.145

0.037

0.038

0.198

#4 Sleep-/circadian rhythm-related

Reported sleep issues

-3.311

1.114

0.009

0.064

0.262

Napping

2.893

1.118

0.009

0.068

0.305/
0.271

*p>.05, not includedin R2

Table2. Summary of the stepwise multiple regression analysis for the final models of fatigue, alertness and daytime sleepiness

Discussion

Fatigue, impaired alertness and daytime
sleepiness are increasingly pervasive and
clinically relevant manifestations in patients
with concussion/mTBI [32-34]. Discerning
patterns of integrative relationships among
elements is critical for defining complex states better, and proper differentiation between them
is essential for future progress in developing
interventions. Grouping elements across simpler
organizational themes allowed us to begin with
associations, followed by statistical models at
various hierarchical levels of aggregation, to
explicate the physiological, pathological and
behavioral processes of fatigue, alertness, and
daytime sleepiness in concussion/mTBI. The
study showed that although these three states
are different constructs in concussion/mTBI,
they share covariates. Fitting the final statistical
models with variables spanning across individual
models at the p ≤ .1 aggregation level (i.e., to
provide a more generic discussion) revealed the
elements across levels of organization, which
were initially viewed as associations, but also
suggested temporal mechanisms to explicate the
basis of the three states.

Our final model showed that fatigue can
be explained by depressive illness and/or its
treatments, insomnia disorder, the influence
of the circadian and homeostatic drive at the
time of reporting, and environmental state,
potentially depicting protection from harm at work. Likewise, alertness shared the depressive
illness aggregate, and its explanatory factors
spanned across components related to brain
health (i.e., restless leg’s, balance issues), together
explaining 41% of the variance. Conversely,
daytime sleepiness, conceptualized as “representing
different levels of ‘somnificity’ that most people
encounter as part of their daily lives” and defined as
“the general characteristic of a posture, activity and
situation that reflects its capacity to facilitate sleeponset
in the majority of subjects” [35], is associated
with the cultural/environmental component,
certain behaviours, sleep-related variables, and
somatization in general.

▪ Fatigue

Multiple factors of fatigue (i.e., depression,
anxiety, insomnia, etc.) are consistently reported
in populations with various neurological
disorders [36,37]. The extent to which these
factors are etiologically related in patients
with concussion/mTBI remains unclear. Our
depression measure (PHQ-9) features a sleep
item (i.e., trouble falling or staying asleep, or
sleeping too much) within it, which, when it was
removed from the analyses, resulted in a stronger
covariate effect of insomnia on both, fatigue and
alertness, and a weaker effect of depression. The
high coincidence and overlapping depressive
and insomnia symptoms may suggest a common
neurobiology, where mechanisms contributing
to abnormal sleep patterns in concussion/
mTBI making the individual more susceptible
to depression [18]. Likewise, the TCA covariate
effect is difficult to interpret: TCAs differ not
only in their relative effects in blocking serotonin
versus norepinephrine reuptake and the degree
of antagonism of H1 histamine receptors, but
also in their effects on depressed patients, which
are reported to be variable [38,39]. Nevertheless,
our results are important, as earlier research
reported that this group of medications can activate
the cytokine system, as manifested by sickness,
depressive behavior, and chronic fatigue [40]. The
immunomodulatory effects [41] of psychoactive
medications as it relates to fatigue should be
investigated further in concussion/mTBI.

We found that fatigue is less profound during
the day than the morning or night, consistent with
the energy allocation model, outlining fluctuations
in performance capacity over the day [42], Earlier
research suggested that a more regular a more
regular day-to-day pattern of rest and activity
relates to lower fatigue and depression scores
[43-45]. Variation in day-to-day rest and activity pattern in persons with concussion/mTBI is,
therefore, important to consider in future studies
investigating perceived states.

The covariate effect of TSI on fatigue is consistent
with previous research [46,47], reinforcing the
concept of time in concussion-related fatigue.
Our findings regarding associations between
fatigue and number of work-related injuries
within the past five years in the final regression
model adjusted for age, sex, weekly income,
depression, insomnia, number of comorbid
conditions, medication effect, and TSI, need
to be replicated in a longitudinal study before
causal conclusions can be drawn; they could
imply, however, that fatigued persons are more
at risk of unsafe performance and inappropriate
strategies in the workplace and therefore, more
at risk of injury [48,49]. Given that almost every
second worker performed shift work at the time
of their injury, and almost 8% believed their
involvement in the most recent accident with
brain injury outcome was due to sleepiness, such
implications are likely, and suggest that frequent
injuries indicate the state of the individuals’
vigilance networks [50]. Etiologically, however,
such factors as repetitive injuries could decrease
a person’s responsiveness to stressors associated
with a new injury; subsequent perpetuating
factors could catalyze progression to chronic
fatigue and generate sickness behavior [51].
These findings support the compelling evidence
that fatigue may compromise safety [51] and
suggest its covariates need to be managed
carefully in concussion/mTBI.

▪ Alertness

The covariate effect of depression and TSI
on fatigue and alertness observed in our
results supports the notion of shared variance
between these constructs in concussion/mTBI.
Absolute value correlations between fatigue
and alertness in our sample were moderate
(rho=-.61); equivalent, in variance terms, to
a shared variance of 10-50% [52]. Previous
studies raised several hypotheses including
neurotransmitter imbalance, dysregulation
of the hypothalamic- pituitary-adrenal axis,
and genetic polymorphism to explain the
mechanisms involved in post- concussive sleep
regulation and psychiatric illness [53]. Thus,
recurrence or worsening of sleep as a result of
concussion/mTBI could account for the relapse
of mood symptoms and also treatment resistance
[54]. Since around 69% of our sample expressed
clinical insomnia, continued study of sleep in concussion/mTBI is appropriate.

Balance issues and RL (a sensory-motor disorder,
a central characteristic of which is worsening of
symptoms during the evening or night [54])
were independently associated with decreased
alertness. Generally, imbalance results from
disorders of the spinal cord (spinocerebellar
pathway) or vestibular input, the integration
of these inputs in the brainstem, or the motor
output to the spinal neurons controlling axial
or proximal muscles [55]. Diffuse axonal injury
(DAI) as a result of strain in the axonal direction
during the injury event with mechanism such
as struck against an object or structure [56],
can affect any combination of central vestibular
pathways in the brain stem, cortico-cerebral
or cerebellar tracts, resulting in symptoms
of imbalance [57], reinforcing our findings
and accounting for our observed relationship.
Restless legs symptoms associate with core body
temperature, and salivary melatonin secretion in
the general population [58]. While we did not
collect information about core body temperature or
salivary melatonin secretion, the strong indication
of circadian rhythm disturbances in our sample
[18], could reasonably explain the results.

Alertness appeared to be sex-dependent in
our final fully adjusted model, with males
being more alert than females. In the general
population, males are reportedly more vigilant
than females throughout life [59-61]. However,
other researchers observed that differences
in alertness between males and females vary
diurnally [62], and are result of the less intense
endogenous control of circadian rhythms in
women, with environmental and sociocultural
influences having a modulating role [62]. Our
results reinforce the notion that research focusing
on unobservable constructs that are subject to
homeostatic or chronophysiological functions
should control for time of data collection and sex.
At the same time, sex differences were observed
in our final model of alertness, controlled for age,
time of assessment, and other relevant covariates.
Further research focusing on differences between
men and women in homeostatic sleep drive, as
an opposed function to circadian-dependent
drive for alertness during waking day, is timely.

▪ Sleepiness

It is generally accepted that degree of daytime
sleepiness is directly related to the duration of
nocturnal sleep and wakefulness, a primary
factor of homeostatic sleep drive [63]. Individual
differences in tolerance of sleep loss are well known [64,65]. We did not collect data on
duration of wakefulness (i.e., sleep-wake history)
the day preceding investigation, to understand
the strength of homeostatic sleep drive or
perceived level of sleepiness in our research
participants. This might explain why the
majority of the variance in daytime sleepiness
remained unexplained in our fully adjusted
multivariable model, and may (if prolonged) be
responsible for the elevated mean ESS score for
the entire cohort of patients in this study (both
males and females), comparable to that reported
for the general community (8.67 ± 5.66, with
8.53 ± 5.69 for men and 8.89 ± 5.70 for
women; vs 6.3 ± 3.5 and - 5.9 ± 2.2, respectively
[66,67]. In TBI patients, central nervous system
pathology with hypocretin/orexin deficiency
is thought to underlie daytime sleepiness [68].
We did not take cerebrospinal fluid samples to
examine this relationship; however, we had no
narcolepsy cases in our sample, as determined
by the Swiss Narcolepsy Scale (SNS) and clinical
examination. In all cases of excessive sleepiness,
our participants also had elevated scores on
the STOP-Bang measure, which could explain
the elevated sleepiness. The mean ESS scores
in our sample were similar to those reported
in larger population-based studies in patients
with respiratory disturbance index (RDI) of 15-
30 [69]. Interestingly, higher rates of daytime
sleepiness are reported in selected populations:
when the ESS was completed by 740 dayworkers
in eight industrial plants in Israel, 23%
of respondents had scores greater than ten [70].
Hesselbacher and colleagues reported that the
ESS scores are influenced not only by RDI, but
also by sex, ethnicity, and body morphometry
[71]. Being born in Canada and having English
as first language were highlighted in our study
as covariates of daytime sleepiness. While they
cannot be regarded as substitutes for ethnicity, they
may suggest a cultural mechanism (i.e., different
subgroups of respondents interpret items differently,
or social situations “as a passenger” or “in a theatre
or a meeting” refer to specific social contexts, the
frequency of which might not be sufficient to
allow some cultural subgroups in Canada to make
a valid assessment) [72]. Furthermore, sleepy
persons for whom English is not a first language,
or who do not read often, might choose “would
never doze” for that question, answering it directly
or hypothetically. The cultural dimorphism
revealed in our study with regard to daytime
sleepiness and its measurement, may therefore
challenge the universality of the daytime
sleepiness construct depicted by the ESS across non-clinical and clinical populations with various
cultural backgrounds and should therefore be
further explored. This is particularly relevant in
a Canadian context, given the linguistic diversity
of this nation and the recent increase in the
number of immigrants with languages other
than English [73-75].

Napping in our study of middle-aged persons
with concussion/mTBI has been identified as a
covariate of daytime sleepiness, independent of
age, sex, education, sleep disorder, somatoform
disorder or mechanism of brain injury. Should
napping be viewed as potentially protective or
as an independent covariate of risk for daytime
sleepiness? Given our cross-sectional study
design, we cannot answer this question directly.
Our complex association matrix showed no
association between daytime sleepiness and either
multimorbidity or multipharmacy, mechanism
of injury, LOC, PTA, RL severity, or sex, but
moderate associations with reported sleep-related
issues at assessment. Therefore, our evidence
continues to support the notion that napping in
the chronic phase of concussion does not improve
daytime sleepiness, but could adversely affect the
ability to fall asleep and could be associated with less
slow-wave sleep, which is viewed as most restorative
[76,77]. Future longitudinal studies are needed to
validate our results.

Finally, somatoform disorder, in which patients
present with multiple physical complaints
to different organ systems [78], remained in
our final model as an independent covariate
of daytime sleepiness. Patients with multiple
somatic complaints that cannot be explained
by a known medical condition or by the
effect of alcohol/recreational or prescription
drugs constitute 5% of the general population
[79], but in our sample such disorders were
documented in the medical files of 28%,
which is significantly higher. It cannot be
determined whether this high prevalence is
attributable to the diffuse brain injury, comorbid
disorders, drug interactions due to regimes
initiated independently by different treatment
providers, or the impulsiveness of patients with
somatization disorder in demanding formal
diagnosis after concussion/mTBI. Our methods
can only undermine the hypothesis rather than
answer this question. Longitudinal studies with
baseline assessment as early as possible after the
injury are needed.

Strength and limitations

Strength of this study is its interdisciplinary
approach, crossing neural, psychosocial, clinical,
and behavioral levels of complex constructs that
could be context-dependent, making it possible
to identify associations on multiple levels
and thus improving the quality of inductive
inferences. A strength of the correlative approach
lies in identifying novel previously-undescribed
associations that could be replicated and worthy
of further study. The multivariate techniques
used allowed the constructs to be reduced to
more meaningful functional parts. During the
development phase of the project we formulated
concepts and hypotheses based on a synthesis
of relevant discoveries across various disciplines
[16,80]. Abstract constructs arising from this
research provide a means of understanding highly
complex perceived states, helping us to think
about elements from different levels of inference
and thus increasing the comprehensiveness and
relevance of hypotheses concerning complex
unobservable constructs. Also, the diagnoses
of concussion/mTBI were made by a team of
clinicians trained in neurology, psychiatry,
psychology and other relevant disciplines. All
diagnoses were provided to the insurer and
therefore indicate liability.

Recruitment to the study was not random; the
selected participants had been gainfully employed
at the time of concussion/mTBI and all had
job to return to, but continued to experience
symptoms interfering with their functioning. In
addition to the collected data pertinent to our
research questions, we had access to all medical
assessments and pre-morbid histories. While
we had reasonably high response rates, many
potential research participants were not enrolled
owing to their lack of informed consent or interest
in the research. We retrospectively reviewed the
charts of the consecutive list of participants
assessed in the same clinic in 2003 to ensure our
sample was representative. Nevertheless, there
could have been selection bias towards those with
more significant distress, those who experienced
less significant physical or cognitive limitations,
or those who wanted to understand the cause of
their ongoing difficulties; although bias could
have lain in the other direction. Nevertheless,
the TSI, injury severity, socio- demographic
and occupational characteristics of our study
group suggest that our findings are likely to be
generalizable to an entire recruitment cohort of
insured persons with persistent symptoms after
concussion/mTBI.

We used PR measures to study fatigue, alertness, and sleepiness in concussion/mTBI. Earlier
study indicates it is easier to perceive fatigue
or lack of energy than any degree of sleepiness
[81]. The perception of sleepiness can also be
influenced by the duration of sleep deprivation
(i.e., individuals could have adjusted to their
long-term impairment, making them less likely
to recognize their degree of sleepiness, instead
reporting tiredness). To mitigate these issues, we
attempted to distinguish covariates of daytime
sleepiness, fatigue, and impaired alertness by
collecting a variety of data related to history of
sleep function, and we also utilized standardized
scales with items enabling us to differentiate
among the three constructs. Nevertheless, it
remains unclear how well the items within our
measures reflected the constructs under study
as we relied on how the measures of complex
constructs were depicted by those who developed
them [25-27]. We provided information on
the internal consistency of all PR data, and the
results show their use in our sample to have
been appropriate. Non-response bias occurred
at 5% on average across all measures, which is
low for PR measurements. Nonetheless, further
description of the psychometric properties of the
FSS, THAT and ESS in concussion/mTBI is
needed, especially those pertaining to construct
validity and reliability. Although our sample
size was adequate to allow accurate estimation
of regression coefficients, standard errors, and
confidence intervals in all linear regression
models, and we had reasonably high response
rates, many potential research participants were
not enrolled owing to their lack of informed
consent or interest in the research. Confirmation
of our results in larger studies is warranted.
Finally, this study highlighted the factors that
were associated with perceived states at the
moment of investigation (i.e., cross-sectional
relationship); the longitudinal relationships
between these factors and studied outcomes
remain to be determined.

Conclusion

Our study suggests that fatigue, alertness and
daytime sleepiness in concussion/mTBI may
not be solely explained by socio-demographic,
brain-injury-, medical-, sleep-related variables,
or medication/drug effects. The analyses across
domains provide unique opportunities for
future research investigating complex perceived
states that tend to be multiply determined.
Similar conclusions were made earlier in Project
Leonardo [82], an interdisciplinary disease and
care management model developed for patients
with heart failure and diabetes, pointing to the
value of multidisciplinary collaborations and
evaluations of all aspects of patient health and
functioning when faced with persistent symptoms
in chronic disorders. Sufficient understanding of
how fatigue, alertness, and daytime sleepiness are
related in concussion/mTBI is important for a
broader health and safety management system,
in particular regarding release to return to work
after injury. Further attention to potential
pathways in longitudinal studies is key to
determining how to manage these states and also
to the search for effective solutions.

Conflict of Interest and Source of Funding

The authors have no conflict of interest to declare
pertaining to this work.

Our study had no external funding source. The
first author was supported by the 2013/2015
Frederick Banting and Charles Best Doctoral
Research Award from the Canadian Institutes of
Health Research and the postdoctoral fellowship
from the Faculty of Occupational Science and
Occupational Therapy at the University of
Toronto. Angela Colantonio was supported by
the Canadian Institutes for Health Research
Grant–Institute for Gender and Health (#CGW-
126580). The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.

References

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